Contact Info: Improving Decision Making: The use of simple heuristics Dr. Guillermo Campitelli Cognition Research Group Edith Cowan University

Contact Info: 3 ways of improving decision making Acquiring knowledge (specific) –Example: chess grand masters Using formal methods –Example: Rules of logic, Statistics, Decision Theory Using simple heuristics –Example: Recognition Heuristic

Contact Info: Simple heuristics Is using simple heuristics a sound strategy? –Research by Gigerenzer and others presents evidence that people use simple heuristics –There is also evidence that using simple heuristics, in some circumstances, is better than using complex strategies

Contact Info: Simon’s (1955) bounded rationality –Limitations of the cognitive system –Good adaptation to the environment People use fast-and-frugal heuristics that “make us smart” (Gigerenzer et al., 1999) Ecological rationality (Gigerenzer and colleagues)

Contact Info: If one of two objects is recognised and the other is not, then infer that the recognised object has the higher value with respect to the criterion Which city has more inhabitants: –Hamburg or Solingen? Recognition Heuristic: If Hamburg is recognised and Solingen is nor recognised, choose Hamburg as the city with more inhabitants. Recognition Heuristic

Contact Info: What happens when using the RH is clearly a bad strategy? –Example: Vatican City against an unrecognised city Recognition Heuristic

Contact Info: RH is the default strategy Hypotheses of why RH is not always used: –H1: Threshold hypothesis –H2: Matching hypothesis –H3: Suspension hypothesis: –The non-use of RH is related to object-specific knowledge that is at odds with recognition (e.g., recognition of a city with a very small population) Suspension of the Recognition Heuristic (Pachur & Hertwig, 2006)

Contact Info: Cities environment –Investigate the non-use of RH –Run a model comparison between models that involve RH, suspension of RH, and knowledge beyond recognition Goals of the study

Contact Info: Cities environment –Creation of an environment of low recognition validity Mean recognition validity =.47 Recognition correlation =.04 –Material 8 cities 28 pairs –Tasks Choice: “Which of these cities has a higher number of inhabitants?” Recognition: “Did you know that there was a city with such name, before participating in the experiment?” –Participants 59 psychology students at Universidad Abierta Interamericana, Buenos Aires, Argentina –Variables Popularity (high, low) Population (large, small) Methods

Contact Info: Analyses χ 2 analyses of choices of each city as a function of population and popularity Model comparison Maximum Likelihood Estimation (MLE), Bayesian Inference Criterion (BIC) Deterministic Probabilistic Methods

Contact Info: Cities: Design Population of each city in millions of inhabitants.

Contact Info: Cities-Results: Recognition Proportion of participants that recognise each city

Contact Info: Cities-Results: Choices Proportion of choices of each city across all participants (59) and pairs (7) | Χ 2 (1) = , p <.0007

Contact Info: Number of choices as a function of popularity and size

Contact Info: Results averaged across participants: –Recognition: M =.57 (SD =.12) –Choice accuracy: M =.54 (SD =.10) Use of RH –78 % of choices across participants and pairs Results

Contact Info: RHg –Recognition heuristic + guess RHsg –Recognition heuristic + suspension + guess RHsur –Recognition heuristic + suspension + unrecognised KH –Knowledge Heuristic If one knows more about one city than the other, one should choose the city that one knows as the one with the higher value in the criterion CK –Criterion Knowledge RND –Random Models

Contact Info: Knowledge Heuristic: Yahoo Log 10 of number of pages the name of the city appears in the internet, using Yahoo search engine

Contact Info: Values of both items in each pair Difference between values Probability of choosing Item 1 given the model Probability of actual choice given the model –2 types of probability models: All or none Probabilistic Log-likelihood BIC All the previous calculations in each participant Classification of participants as users of the model with lower BIC Procedure of calculations

Contact Info: All or none Probabilistic Probability of choosing Item 1 in pair j, given model k

Contact Info: All or none Probabilistic Probability of actual choice in pair j given model k

Contact Info: Sd.1

Contact Info: Sd.5

Contact Info: Sd 1

Contact Info: Log-Likelihood Bayesian Information Criterion (BIC) Model comparison

Contact Info: Model comparison: Results Models TypeMeasureRHgRHsgRhsurKHCKRND All or NoneMean LL MeanBIC # wins ProbabilisticMean LL MeanBIC Mean σ NA # wins

Contact Info: RH was the most popular strategy The use of RH was not adaptive The use of strategies is independent from the ecological validity of the strategy Discussion

Contact Info: People sometimes use available strategies, and sometimes guess RH is one of these strategies The use of strategies does not seem to be related to their usefulness Conclusion

Contact Info: Can we improve decision making by using simple heuristics? –Yes, but a degree of knowledge is required to successfully decide when it is appropriate to use the simple heuristic Conclusion

Contact Info: Thank you!